PHARAOH: A collaborative crowdsourcing platform for phenotyping and regional analysis of histology
Abstract Deep learning has proven capable of automating key aspects of histopathologic analysis. However, its context-specific nature and continued reliance on large expert-annotated training datasets hinders the development of a critical mass of applications to garner widespread adoption in clinica...
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2025-01-01
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| Online Access: | https://doi.org/10.1038/s41467-024-55780-z |
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| author | Kevin Faust Min Li Chen Parsa Babaei Zadeh Dimitrios G. Oreopoulos Alberto J. Leon Ameesha Paliwal Evelyn Rose Kamski-Hennekam Marly Mikhail Xianpi Duan Xianzhao Duan Mugeng Liu Narges Ahangari Raul Cotau Vincent Francis Castillo Nikfar Nikzad Richard J. Sugden Patrick Murphy Safiyh S. Aljohani Philippe Echelard Susan J. Done Kiran Jakate Zaid Saeed Kamil Yazeed Alwelaie Mohammed J. Alyousef Noor Said Alsafwani Assem Saleh Alrumeh Rola M. Saleeb Maxime Richer Lidiane Vieira Marins George M. Yousef Phedias Diamandis |
| author_facet | Kevin Faust Min Li Chen Parsa Babaei Zadeh Dimitrios G. Oreopoulos Alberto J. Leon Ameesha Paliwal Evelyn Rose Kamski-Hennekam Marly Mikhail Xianpi Duan Xianzhao Duan Mugeng Liu Narges Ahangari Raul Cotau Vincent Francis Castillo Nikfar Nikzad Richard J. Sugden Patrick Murphy Safiyh S. Aljohani Philippe Echelard Susan J. Done Kiran Jakate Zaid Saeed Kamil Yazeed Alwelaie Mohammed J. Alyousef Noor Said Alsafwani Assem Saleh Alrumeh Rola M. Saleeb Maxime Richer Lidiane Vieira Marins George M. Yousef Phedias Diamandis |
| author_sort | Kevin Faust |
| collection | DOAJ |
| description | Abstract Deep learning has proven capable of automating key aspects of histopathologic analysis. However, its context-specific nature and continued reliance on large expert-annotated training datasets hinders the development of a critical mass of applications to garner widespread adoption in clinical/research workflows. Here, we present an online collaborative platform that streamlines tissue image annotation to promote the development and sharing of custom computer vision models for PHenotyping And Regional Analysis Of Histology (PHARAOH; https://www.pathologyreports.ai/ ). Specifically, PHARAOH uses a weakly supervised, human-in-the-loop learning framework whereby patch-level image features are leveraged to organize large swaths of tissue into morphologically-uniform clusters for batched annotation by human experts. By providing cluster-level labels on only a handful of cases, we show how custom PHARAOH models can be developed efficiently and used to guide the quantification of cellular features that correlate with molecular, pathologic and patient outcome data. Moreover, by using our PHARAOH pipeline, we showcase how correlation of cohort-level cytoarchitectural features with accompanying biological and outcome data can help systematically devise interpretable morphometric models of disease. Both the custom model design and feature extraction pipelines are amenable to crowdsourcing, positioning PHARAOH to become a fully scalable, systems-level solution for the expansion, generalization and cataloging of computational pathology applications. |
| format | Article |
| id | doaj-art-11156d46defe4bacbd6d8e9c2895fe18 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Nature Communications |
| spelling | doaj-art-11156d46defe4bacbd6d8e9c2895fe182025-01-19T12:29:52ZengNature PortfolioNature Communications2041-17232025-01-0116111210.1038/s41467-024-55780-zPHARAOH: A collaborative crowdsourcing platform for phenotyping and regional analysis of histologyKevin Faust0Min Li Chen1Parsa Babaei Zadeh2Dimitrios G. Oreopoulos3Alberto J. Leon4Ameesha Paliwal5Evelyn Rose Kamski-Hennekam6Marly Mikhail7Xianpi Duan8Xianzhao Duan9Mugeng Liu10Narges Ahangari11Raul Cotau12Vincent Francis Castillo13Nikfar Nikzad14Richard J. Sugden15Patrick Murphy16Safiyh S. Aljohani17Philippe Echelard18Susan J. Done19Kiran Jakate20Zaid Saeed Kamil21Yazeed Alwelaie22Mohammed J. Alyousef23Noor Said Alsafwani24Assem Saleh Alrumeh25Rola M. Saleeb26Maxime Richer27Lidiane Vieira Marins28George M. Yousef29Phedias Diamandis30Princess Margaret Cancer CentrePrincess Margaret Cancer CentrePrincess Margaret Cancer CentrePrincess Margaret Cancer CentrePrincess Margaret Cancer CentrePrincess Margaret Cancer CentrePrincess Margaret Cancer CentrePrincess Margaret Cancer CentreDepartment of Computing and Software, McMaster UniversityDepartment of Computing and Software, McMaster UniversityPrincess Margaret Cancer CentreDepartment of Laboratory Medicine and Pathobiology, University of TorontoAxe neurosciences du Centre de recherche du Centre hospitalier universitaire (CHU) de Québec-Université Laval, et Département de biologie moléculaire, biochimie et pathologie de l’Université LavalDepartment of Laboratory Medicine and Pathobiology, University of TorontoDepartment of Pathology and Molecular Medicine, McMaster UniversityPrincess Margaret Cancer CentreDepartment of Laboratory Medicine and Pathobiology, University of TorontoDepartment of Pathology, College of Medicine, Taibah UniversityDépartement de pathologie, Université de SherbrookePrincess Margaret Cancer CentreDepartment of Laboratory Medicine and Pathobiology, University of TorontoDepartment of Laboratory Medicine and Pathobiology, University of TorontoDepartment of Pathology and Clinical Laboratory Medicine, King Fahad Medical CityDepartment of Pathology, College of Medicine, Imam Abdulrahman Bin Faisal UniversityDepartment of Pathology, College of Medicine, Imam Abdulrahman Bin Faisal UniversityLaboratory Medicine Program, Department of Pathology, University Health Network, 200 Elizabeth StreetDepartment of Laboratory Medicine and Pathobiology, University of TorontoAxe neurosciences du Centre de recherche du Centre hospitalier universitaire (CHU) de Québec-Université Laval, et Département de biologie moléculaire, biochimie et pathologie de l’Université LavalInstituto D’Or de Pesquisa e Ensino (IDOR)Department of Laboratory Medicine and Pathobiology, University of TorontoPrincess Margaret Cancer CentreAbstract Deep learning has proven capable of automating key aspects of histopathologic analysis. However, its context-specific nature and continued reliance on large expert-annotated training datasets hinders the development of a critical mass of applications to garner widespread adoption in clinical/research workflows. Here, we present an online collaborative platform that streamlines tissue image annotation to promote the development and sharing of custom computer vision models for PHenotyping And Regional Analysis Of Histology (PHARAOH; https://www.pathologyreports.ai/ ). Specifically, PHARAOH uses a weakly supervised, human-in-the-loop learning framework whereby patch-level image features are leveraged to organize large swaths of tissue into morphologically-uniform clusters for batched annotation by human experts. By providing cluster-level labels on only a handful of cases, we show how custom PHARAOH models can be developed efficiently and used to guide the quantification of cellular features that correlate with molecular, pathologic and patient outcome data. Moreover, by using our PHARAOH pipeline, we showcase how correlation of cohort-level cytoarchitectural features with accompanying biological and outcome data can help systematically devise interpretable morphometric models of disease. Both the custom model design and feature extraction pipelines are amenable to crowdsourcing, positioning PHARAOH to become a fully scalable, systems-level solution for the expansion, generalization and cataloging of computational pathology applications.https://doi.org/10.1038/s41467-024-55780-z |
| spellingShingle | Kevin Faust Min Li Chen Parsa Babaei Zadeh Dimitrios G. Oreopoulos Alberto J. Leon Ameesha Paliwal Evelyn Rose Kamski-Hennekam Marly Mikhail Xianpi Duan Xianzhao Duan Mugeng Liu Narges Ahangari Raul Cotau Vincent Francis Castillo Nikfar Nikzad Richard J. Sugden Patrick Murphy Safiyh S. Aljohani Philippe Echelard Susan J. Done Kiran Jakate Zaid Saeed Kamil Yazeed Alwelaie Mohammed J. Alyousef Noor Said Alsafwani Assem Saleh Alrumeh Rola M. Saleeb Maxime Richer Lidiane Vieira Marins George M. Yousef Phedias Diamandis PHARAOH: A collaborative crowdsourcing platform for phenotyping and regional analysis of histology Nature Communications |
| title | PHARAOH: A collaborative crowdsourcing platform for phenotyping and regional analysis of histology |
| title_full | PHARAOH: A collaborative crowdsourcing platform for phenotyping and regional analysis of histology |
| title_fullStr | PHARAOH: A collaborative crowdsourcing platform for phenotyping and regional analysis of histology |
| title_full_unstemmed | PHARAOH: A collaborative crowdsourcing platform for phenotyping and regional analysis of histology |
| title_short | PHARAOH: A collaborative crowdsourcing platform for phenotyping and regional analysis of histology |
| title_sort | pharaoh a collaborative crowdsourcing platform for phenotyping and regional analysis of histology |
| url | https://doi.org/10.1038/s41467-024-55780-z |
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